Building an AI governance framework that actually gets used
- Samson Lingampalli
- Mar 24
- 1 min read
-By Simon Shobrook
Governance frameworks often fail simply because they aren’t put into practice.
Add AI into the mix, and things can quickly spiral out of control.
Policies get written.
Principles have been agreed upon.
Committees get formed.
And then reality happens.
AI systems go live.
Models change.
Risks evolve.
Suppliers update without visibility.
And the “framework” sits in a document somewhere, disconnected from what’s actually happening.
That’s the gap.
If governance isn’t embedded into day-to-day operations, it isn’t governance.It’s something that just sits on a shelf, unused.
This is where most frameworks break down.
They are designed for oversight.
A team needs periodic training, checks and oversight, but AI systems require continuous monitoring, control, and intervention.
Not just:
policies
approvals
sign-offs
But:
real-time visibility
structured audit trails
clear ownership of live systems
And this is exactly the problem RAITracker is built to solve.
RAITracker (RAIT) isn’t another framework.
It’s how governance actually gets done.
A live register of AI systems
Ongoing monitoring of performance and risk
Observability and control
Evidence captured as systems operate, not retrospectively
Clear accountability across teams and suppliers
It turns governance from a concept into something operational.
Because in the UK public sector, accountability doesn’t sit in a policy document.
It sits with named individuals.
And when something goes wrong, the question won’t be:
“Did you have a framework?”
It will be:
“Can you show how this system was governed in practice?”
That’s the difference between shelf-ware and control.
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